frame² is a lightweight sports analytics and content engine built to explain games through process vs result.
the goal is simple:
observe → detect the edge → explain the mechanism → generate insights
example output generated by frame²:
observation
2024 red sox finished 81-81.
mechanism
run differential was neutral, but the offense relied heavily on duran as the lone consistent process driver.
implication
the result and underlying process were aligned — an average team produced an average outcome.
most fans watch the scoreboard.
frame² tries to explain why the scoreboard moved.
the engine focuses on identifying hidden drivers such as:
- contact quality
- plate discipline
- run expectancy swings
- roster structure
- development pipeline
these signals help reveal the mechanism behind outcomes.
explore historical eras and identify what drove each team’s success.
visualize the full history of the franchise, including:
- world series seasons
- playoff runs
- rebuilding periods
- emerging cores
see which players carried each era and how strong the organizational pipeline was.
automatically convert analytics into ready-to-post insights.
planned upgrades for frame²:
- live game ingestion
- automated tilt detection
- multi-sport support (nba / nfl / mlb)
- real-time insight generation
- integration with social publishing pipelines
frame2_mvp/
├── app.py # streamlit dashboard
├── engine.py # edge detection + analysis logic
├── data.py # example game/team inputs
├── posts.py # insight → post generator
├── images/ # README screenshots
├── scripts/ # utilities + helpers
└── requirements.txt # python dependencies
git clone https://github.com/dualityframework-ux/frame2_mvp.git
cd frame2_mvp
pip install -r requirements.txt
streamlit run app.py